From its inception, biometric technology has held the promise of transforming the way society works. One manner in which this may occur is by eliminating token-based identification such as ID cards and credit cards. Numerous system architectures have been developed, and often tried, for various applications ranging from personal security and access control to the purchasing of goods and services in e-commerce transactions. However, to date no commercial biometric application has managed to create both the compelling operational benefits and price/performance ratio necessary to drive consumer demand needed for adoption and long term sustainability in an e-commerce application. Now for the first time, recent technology breakthroughs in biometric performance using ultrasonic fingerprint identification, along with the pervasive and ubiquitous worldwide presence of smartphones, offer the building blocks needed to change the landscape of how e-commerce is conducted.
Biometric system architectures and actual pilot systems have been deployed over a number of years in a variety of e-commerce applications. After years of effort and implementation of operational systems in a number of pilot stores, biometric e-commerce developers failed in their attempts to achieve the price/performance/benefit ratio needed for consumer adoption. A common system architecture developers use is shown in FIG. 1.
FIG. 1 shows a prior art retail checkout lane equipped with a cash register 10 and a credit card swipe terminal 12. A swipe of a credit card sends the ABA (American Banking Association) data contained on the magnetic stripe of the credit card via the network for verification and authorization by third party payment processors. In the prior art, upgrading a checkout lane to support biometric identification required adding a biometric fingerprint reader 14, a UTC (Under the Counter) processor 16, a localized AFIS (Automated Fingerprint Identification System) 18, a centralized AFIS 20, and an enrollment center (not shown).
The prior art system requires a biometric fingerprint reader 14 at each checkout lane for establishing a user's identity. Due to size and cost restrictions, a single-finger fingerprint scanner is utilized. Regardless of the operational mode (identification versus verification), the identification information from a single fingerprint does not provide enough accuracy to facilitate an e-commerce application. Furthermore, human factor issues are heightened with use of a single-finger fingerprint sensor. Specifically, it has been shown repeatedly in a variety of applications that the average user, when placing a single finger onto a fingerprint reader, will have a tendency to roll the finger to the left or right. This creates a situation where fingerprint information from an incorrect portion of the finger may be scanned which can ultimately lead to a false rejection. Careful presentation of the finger a second time, with more attention to finger placement, can overcome this problem but ultimately results in increased processing time at the checkout lane.
A multi-finger (two or more fingers) fingerprint reader not only provides additional biometric data to address system accuracy concerns, but also minimizes the finger presentation issues associated with a single-finger device. For example, it may be very difficult for a user to rotate their entire hand when 4-fingers are lying flat on a fingerprint platen. However, multi-finger optical based fingerprint scanners that are able to image multiple fingers are very expensive and physically large.
Furthermore, the biometric industry recognizes the sensitivity of optical scanners. Contamination that builds up on fingers or on the platen, low humidity, high humidity, exposure to sunlight, and other factors may significantly impact the ability of an optical scanner to obtain a high quality fingerprint image. Thus, independent of the number of fingers an optical sensor is able to scan, the prior art system often obtains compromised images causing the identification attempt to fail.
In an example of the prior art, a single board computer with enough processing power to perform the feature extraction and match functions was placed at each checkout counter (i.e. an AFIS in a box). This was primarily due to the fact that the large volumes of data associated with the raw fingerprint image, the available bandwidth of a network communication link, and the processing power needed by a single server to simultaneously process fingerprint data from the 30+ checkout lanes seen in some larger supermarkets, was unmanageable. The UTC's were expensive, prone to hardware failure, and required obligatory updates on a regular basis.
Stores implementing biometric payment systems required the addition of an AFIS into their information-technology (“IT”) infrastructure. The AFIS was necessary for storing the biometric templates at the time of enrollment, along with the credit and loyalty card information for each consumer. At the point of sale, a consumer would be asked to enter a non-unique PIN code (typically a birth date) which was sent to the AFIS. The AFIS would retrieve the candidate list of all biometric templates associated with that birth date and pass those templates to the UTC for subsequent match identification. In addition to the expense of adding another server to the store's IT infrastructure, also added was the responsibility of supporting and maintaining a new process within the store, which was not a familiar task for a retail merchant.
In addition to localized store AFIS servers 18, a centralized enterprise AFIS 20 was located at some location such as corporate headquarters. The purpose of the enterprise AFIS 20 was to enable a patron to enroll at one store and seamlessly use the technology at another store in a different location. This was done by synchronizing the enrollment data amongst individual stores via a central enterprise AFIS 20. The expense in both hard and soft costs associated with implementing a centralized AFIS 20 at an IT datacenter represented added cost and complexity that was difficult for most retailers to implement.
Although not shown in FIG. 1, a second element may be associated with e-commerce biometric applications; specifically, how to initially enroll into the system a consumer, and his/her credit cards authorized for e-payment. This enrollment process was generally accomplished at a kiosk located at the front of the store. The kiosk was staffed by individuals ready to assist the consumer on the use and benefits of a biometrically authenticated payment system, as well as answer any questions they had. The equipment at the kiosk was essentially everything that was at a checkout lane, with the exception of the cash register. This created significant additional expense, and occupied scarce, high-value floor space in the front of each store. More significantly however, this arrangement generally led to discovery conversations with the consumer, the most important being “Where is my biometric data being stored, how secure is it, and who has access to it?” While the answers to these questions were well thought out, the average consumer was often left with an uneasy feeling about the answers.
The inherent flaws in the prior art system architecture are numerous and can be summarized as: High per/lane hardware costs; High support and maintenance costs; Poor biometric system performance; Compromise of the consumer convenience factor due to the required use of a PIN; and Consumer concerns over data privacy.
All of these factors together resulted in the price/performance/benefit ratio failing to meet that which is required for vendor and consumer adoption, and long term sustainability. Consumer demand remains high as evidenced by many industry studies, but the prior art is unable to satisfy that demand.